COVID-19 biomarkers and their overlap with comorbidities in a disease biomarker data model
Author
Gogate, NikhitaLyman, Daniel
Bell, Amanda
Cauley, Edmund
Crandall, Keith A.
Joseph, Ashia
Kahsay, Robel
Natale, Darren A.
Schriml, Lynn M.
Sen, Sabyasach
Mazumder, Raja
Date
2021-11-05Journal
Briefings in BioinformaticsPublisher
Oxford University PressType
Article
Metadata
Show full item recordAbstract
Abstract In response to the COVID-19 outbreak, scientists and medical researchers are capturing a wide range of host responses, symptoms and lingering postrecovery problems within the human population. These variable clinical manifestations suggest differences in influential factors, such as innate and adaptive host immunity, existing or underlying health conditions, comorbidities, genetics and other factors-compounding the complexity of COVID-19 pathobiology and potential biomarkers associated with the disease, as they become available. The heterogeneous data pose challenges for efficient extrapolation of information into clinical applications. We have curated 145 COVID-19 biomarkers by developing a novel cross-cutting disease biomarker data model that allows integration and evaluation of biomarkers in patients with comorbidities. Most biomarkers are related to the immune (SAA, TNF-∝ and IP-10) or coagulation (D-dimer, antithrombin and VWF) cascades, suggesting complex vascular pathobiology of the disease. Furthermore, we observe commonality with established cancer biomarkers (ACE2, IL-6, IL-4 and IL-2) as well as biomarkers for metabolic syndrome and diabetes (CRP, NLR and LDL). We explore these trends as we put forth a COVID-19 biomarker resource (https://data.oncomx.org/covid19) that will help researchers and diagnosticians alike. © The Author(s) 2021.Sponsors
National Science FoundationIdentifier to cite or link to this item
http://hdl.handle.net/10713/17641ae974a485f413a2113503eed53cd6c53
10.1093/bib/bbab191
Scopus Count
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